TeeDAO: A Decentralized Autonomous Organization for Heterogeneous TEEs

• URL: http://arxiv.org/abs/2606.04912v1
• Date: Wed, 03 Jun 2026 14:09:05 GMT
• Title: TeeDAO: A Decentralized Autonomous Organization for Heterogeneous TEEs
• Abstract summary: Trusted Execution Environments (TEEs) have emerged as a critical technology for safeguarding sensitive data and ensuring code integrity in modern computing systems.<n>This paper introduces TeeDAO, a novel three-layer framework that automatically organizes multiple heterogeneous TEE instances.<n>We show that TeeDAO achieves up to 1.8x higher key-value store throughput in a large cluster with 61 nodes compared to state-of-the-art systems.
• Score: 20.990545504683563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trusted Execution Environments (TEEs) have emerged as a critical technology for safeguarding sensitive data and ensuring code integrity in modern computing systems. However, relying on a single TEE implementation makes systems vulnerable to a central point of attack. Building distributed-trust systems leveraging heterogeneous TEEs helps disperse trust but still faces threats from centralized management and adaptive mobile adversaries. To address these challenges, this paper introduces TeeDAO, a novel three-layer framework that automatically organizes multiple heterogeneous TEE instances and provides unified interfaces to support diverse applications, while ensuring long-term guarantees of availability, integrity, and confidentiality. TeeDAO couples BFT-ordered governance with heterogeneity-aware Distributed Proactive Secret Sharing (DPSS) and Secure Multi-Party Computation (MPC) so that attestation-driven committee changes are consistently reflected in secret recovery, resharing, and computation across a dynamic committee of heterogeneous TEEs. We implement a prototype of TeeDAO, integrating COBRA's DPSS scheme with the HotStuff BFT consensus protocol, and adapt it for Intel SGX, TDX, and Hygon CSV. Evaluations demonstrate that TeeDAO achieves up to 1.8x higher key-value store throughput in a large cluster with 61 nodes compared to state-of-the-art systems, efficient autonomous management, and minimal computation overhead (<18%) for multi-party computation tasks.

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